{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c7d8f80",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[ARGUMENTS]\n",
      " Namespace(K=100, approximater_type='None', batch_size=8, beta=0.01, checkpoint_dir='checkpoints', checkpoint_name='best_acc_k10_le-5_tau0.6_beta0.001.tar', chunk_size=2, cuda=True, data_dir='dataset/Dataset_BUSI_AN/train/images', dataset='mnist', default_dir='.', env_name='main', epoch=100, explainer_type='cnn4', load_checkpoint='', lr=1e-06, mode='test', model_name='original_BUSI6.ckpt', num_avg=4, save_checkpoint=True, save_image=True, summary_dir='summary', tau=0.7, tensorboard=True)\n",
      "/workspace/MTL-IBA/MTL-VIBI\n",
      "torch.Size([8, 1, 224, 224])\n",
      "torch.Size([8, 1, 224, 224])\n",
      "torch.Size([8, 1, 224, 224])\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 1\n",
      "global iter 125\n",
      "IZY:2.53 IZX:0.64\n",
      "acc:0.6719 avg_acc:0.6719\n",
      "acc_fixed:0.6719 avg_acc_fixed:0.6719\n",
      "vmi:-0.1008 avg_vmi:-0.1007\n",
      "vmi_fixed:-0.1008 avg_vmi_fixed:-0.1008\n",
      "\n",
      "epoch:1\n",
      "Time spent is 40.505823612213135\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 2\n",
      "global iter 250\n",
      "IZY:2.53 IZX:0.54\n",
      "acc:0.6719 avg_acc:0.6719\n",
      "acc_fixed:0.6719 avg_acc_fixed:0.6719\n",
      "vmi:-0.1018 avg_vmi:-0.1019\n",
      "vmi_fixed:-0.1019 avg_vmi_fixed:-0.1019\n",
      "\n",
      "epoch:2\n",
      "Time spent is 78.68499565124512\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 3\n",
      "global iter 375\n",
      "IZY:2.53 IZX:0.45\n",
      "acc:0.6719 avg_acc:0.6719\n",
      "acc_fixed:0.6719 avg_acc_fixed:0.6719\n",
      "vmi:-0.1017 avg_vmi:-0.1019\n",
      "vmi_fixed:-0.1021 avg_vmi_fixed:-0.1021\n",
      "\n",
      "epoch:3\n",
      "Time spent is 114.5518445968628\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 4\n",
      "global iter 500\n",
      "IZY:2.52 IZX:0.36\n",
      "acc:0.6719 avg_acc:0.6719\n",
      "acc_fixed:0.6719 avg_acc_fixed:0.6719\n",
      "vmi:-0.1034 avg_vmi:-0.1034\n",
      "vmi_fixed:-0.1038 avg_vmi_fixed:-0.1038\n",
      "\n",
      "epoch:4\n",
      "Time spent is 152.74937295913696\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 5\n",
      "global iter 625\n",
      "IZY:2.52 IZX:0.29\n",
      "acc:0.6719 avg_acc:0.6719\n",
      "acc_fixed:0.6562 avg_acc_fixed:0.6562\n",
      "vmi:-0.1039 avg_vmi:-0.1036\n",
      "vmi_fixed:-0.1041 avg_vmi_fixed:-0.1041\n",
      "\n",
      "epoch:5\n",
      "Time spent is 194.88365268707275\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 6\n",
      "global iter 750\n",
      "IZY:2.52 IZX:0.24\n",
      "acc:0.6719 avg_acc:0.6719\n",
      "acc_fixed:0.6250 avg_acc_fixed:0.6250\n",
      "vmi:-0.1039 avg_vmi:-0.1038\n",
      "vmi_fixed:-0.1045 avg_vmi_fixed:-0.1045\n",
      "\n",
      "epoch:6\n",
      "Time spent is 232.86239671707153\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 7\n",
      "global iter 875\n",
      "IZY:2.52 IZX:0.19\n",
      "acc:0.6719 avg_acc:0.6406\n",
      "acc_fixed:0.4375 avg_acc_fixed:0.4375\n",
      "vmi:-0.1049 avg_vmi:-0.1048\n",
      "vmi_fixed:-0.1056 avg_vmi_fixed:-0.1056\n",
      "\n",
      "epoch:7\n",
      "Time spent is 271.0438268184662\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 8 Time since 5m 21s\n",
      "global iter 1000\n",
      "i:125 IZY:2.51 IZX:0.01\n",
      "acc:0.2500 avg_acc:0.3750\n",
      "acc_fixed:0.3750 avg_acc_fixed:0.3750\n",
      "vmi:-0.0081 avg_vmi:0.0003\n",
      "vmi_fixed:-0.0137 avg_vmi_fixed:-0.0194\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 8\n",
      "global iter 1000\n",
      "IZY:2.52 IZX:0.15\n",
      "acc:0.4844 avg_acc:0.4531\n",
      "acc_fixed:0.4219 avg_acc_fixed:0.4219\n",
      "vmi:-0.1057 avg_vmi:-0.1060\n",
      "vmi_fixed:-0.1070 avg_vmi_fixed:-0.1070\n",
      "\n",
      "epoch:8\n",
      "Time spent is 313.3921043872833\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 9\n",
      "global iter 1125\n",
      "IZY:2.52 IZX:0.12\n",
      "acc:0.4375 avg_acc:0.4844\n",
      "acc_fixed:0.4219 avg_acc_fixed:0.4219\n",
      "vmi:-0.1062 avg_vmi:-0.1057\n",
      "vmi_fixed:-0.1069 avg_vmi_fixed:-0.1069\n",
      "\n",
      "epoch:9\n",
      "Time spent is 351.30948972702026\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 10\n",
      "global iter 1250\n",
      "IZY:2.52 IZX:0.10\n",
      "acc:0.5469 avg_acc:0.4844\n",
      "acc_fixed:0.4219 avg_acc_fixed:0.4219\n",
      "vmi:-0.1056 avg_vmi:-0.1059\n",
      "vmi_fixed:-0.1071 avg_vmi_fixed:-0.1071\n",
      "\n",
      "epoch:10\n",
      "Time spent is 388.16256070137024\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 11\n",
      "global iter 1375\n",
      "IZY:2.52 IZX:0.08\n",
      "acc:0.5312 avg_acc:0.5625\n",
      "acc_fixed:0.4062 avg_acc_fixed:0.4062\n",
      "vmi:-0.1054 avg_vmi:-0.1055\n",
      "vmi_fixed:-0.1069 avg_vmi_fixed:-0.1069\n",
      "\n",
      "epoch:11\n",
      "Time spent is 424.90796279907227\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 12\n",
      "global iter 1500\n",
      "IZY:2.52 IZX:0.07\n",
      "acc:0.5312 avg_acc:0.4688\n",
      "acc_fixed:0.4062 avg_acc_fixed:0.4062\n",
      "vmi:-0.1056 avg_vmi:-0.1058\n",
      "vmi_fixed:-0.1075 avg_vmi_fixed:-0.1075\n",
      "\n",
      "epoch:12\n",
      "Time spent is 462.9626784324646\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 13\n",
      "global iter 1625\n",
      "IZY:2.52 IZX:0.05\n",
      "acc:0.6719 avg_acc:0.5781\n",
      "acc_fixed:0.3750 avg_acc_fixed:0.3750\n",
      "vmi:-0.1046 avg_vmi:-0.1053\n",
      "vmi_fixed:-0.1071 avg_vmi_fixed:-0.1071\n",
      "\n",
      "epoch:13\n",
      "Time spent is 499.219042301178\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 14\n",
      "global iter 1750\n",
      "IZY:2.52 IZX:0.04\n",
      "acc:0.7188 avg_acc:0.6094\n",
      "acc_fixed:0.3906 avg_acc_fixed:0.3906\n",
      "vmi:-0.1043 avg_vmi:-0.1050\n",
      "vmi_fixed:-0.1077 avg_vmi_fixed:-0.1077\n",
      "\n",
      "epoch:14\n",
      "Time spent is 536.2085223197937\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 15\n",
      "global iter 1875\n",
      "IZY:2.52 IZX:0.04\n",
      "acc:0.6562 avg_acc:0.6562\n",
      "acc_fixed:0.4219 avg_acc_fixed:0.4219\n",
      "vmi:-0.1042 avg_vmi:-0.1043\n",
      "vmi_fixed:-0.1074 avg_vmi_fixed:-0.1074\n",
      "\n",
      "epoch:15\n",
      "Time spent is 570.4216029644012\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 16 Time since 10m 20s\n",
      "global iter 2000\n",
      "i:125 IZY:2.51 IZX:0.01\n",
      "acc:0.2500 avg_acc:0.6250\n",
      "acc_fixed:0.8750 avg_acc_fixed:0.5000\n",
      "vmi:-0.0416 avg_vmi:-0.0251\n",
      "vmi_fixed:-0.0118 avg_vmi_fixed:-0.0359\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 16\n",
      "global iter 2000\n",
      "IZY:2.52 IZX:0.03\n",
      "acc:0.5312 avg_acc:0.6719\n",
      "acc_fixed:0.3750 avg_acc_fixed:0.3750\n",
      "vmi:-0.1051 avg_vmi:-0.1049\n",
      "vmi_fixed:-0.1084 avg_vmi_fixed:-0.1084\n",
      "\n",
      "epoch:16\n",
      "Time spent is 611.0255370140076\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 17\n",
      "global iter 2125\n",
      "IZY:2.52 IZX:0.03\n",
      "acc:0.6406 avg_acc:0.6719\n",
      "acc_fixed:0.4062 avg_acc_fixed:0.4062\n",
      "vmi:-0.1049 avg_vmi:-0.1048\n",
      "vmi_fixed:-0.1090 avg_vmi_fixed:-0.1090\n",
      "\n",
      "epoch:17\n",
      "Time spent is 649.1776969432831\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 18\n",
      "global iter 2250\n",
      "IZY:2.52 IZX:0.02\n",
      "acc:0.6250 avg_acc:0.6719\n",
      "acc_fixed:0.3750 avg_acc_fixed:0.3750\n",
      "vmi:-0.1049 avg_vmi:-0.1039\n",
      "vmi_fixed:-0.1087 avg_vmi_fixed:-0.1087\n",
      "\n",
      "epoch:18\n",
      "Time spent is 683.8820416927338\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 19\n",
      "global iter 2375\n",
      "IZY:2.52 IZX:0.02\n",
      "acc:0.6875 avg_acc:0.6719\n",
      "acc_fixed:0.3906 avg_acc_fixed:0.3906\n",
      "vmi:-0.1035 avg_vmi:-0.1038\n",
      "vmi_fixed:-0.1091 avg_vmi_fixed:-0.1091\n",
      "\n",
      "epoch:19\n",
      "Time spent is 717.7424075603485\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 20\n",
      "global iter 2500\n",
      "IZY:2.52 IZX:0.02\n",
      "acc:0.5781 avg_acc:0.6250\n",
      "acc_fixed:0.4375 avg_acc_fixed:0.4375\n",
      "vmi:-0.1030 avg_vmi:-0.1029\n",
      "vmi_fixed:-0.1089 avg_vmi_fixed:-0.1089\n",
      "\n",
      "epoch:20\n",
      "Time spent is 753.7015612125397\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 21\n",
      "global iter 2625\n",
      "IZY:2.53 IZX:0.02\n",
      "acc:0.6406 avg_acc:0.5938\n",
      "acc_fixed:0.4375 avg_acc_fixed:0.4375\n",
      "vmi:-0.1019 avg_vmi:-0.1026\n",
      "vmi_fixed:-0.1090 avg_vmi_fixed:-0.1090\n",
      "\n",
      "epoch:21\n",
      "Time spent is 790.2965176105499\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 22\n",
      "global iter 2750\n",
      "IZY:2.53 IZX:0.01\n",
      "acc:0.6406 avg_acc:0.6562\n",
      "acc_fixed:0.4688 avg_acc_fixed:0.4688\n",
      "vmi:-0.1018 avg_vmi:-0.1017\n",
      "vmi_fixed:-0.1087 avg_vmi_fixed:-0.1087\n",
      "\n",
      "epoch:22\n",
      "Time spent is 824.7382898330688\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 23\n",
      "global iter 2875\n",
      "IZY:2.53 IZX:0.01\n",
      "acc:0.7031 avg_acc:0.6875\n",
      "acc_fixed:0.4531 avg_acc_fixed:0.4531\n",
      "vmi:-0.1000 avg_vmi:-0.1007\n",
      "vmi_fixed:-0.1093 avg_vmi_fixed:-0.1093\n",
      "\n",
      "epoch:23\n",
      "Time spent is 864.6588530540466\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 24 Time since 15m 10s\n",
      "global iter 3000\n",
      "i:125 IZY:2.55 IZX:0.01\n",
      "acc:0.6250 avg_acc:0.8750\n",
      "acc_fixed:0.2500 avg_acc_fixed:0.2500\n",
      "vmi:0.0275 avg_vmi:0.0131\n",
      "vmi_fixed:-0.0175 avg_vmi_fixed:-0.0151\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 24\n",
      "global iter 3000\n",
      "IZY:2.53 IZX:0.01\n",
      "acc:0.7188 avg_acc:0.5938\n",
      "acc_fixed:0.4688 avg_acc_fixed:0.4688\n",
      "vmi:-0.1002 avg_vmi:-0.1006\n",
      "vmi_fixed:-0.1100 avg_vmi_fixed:-0.1100\n",
      "\n",
      "epoch:24\n",
      "Time spent is 901.4905707836151\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 25\n",
      "global iter 3125\n",
      "IZY:2.53 IZX:0.01\n",
      "acc:0.6562 avg_acc:0.6094\n",
      "acc_fixed:0.4375 avg_acc_fixed:0.4375\n",
      "vmi:-0.0996 avg_vmi:-0.0992\n",
      "vmi_fixed:-0.1103 avg_vmi_fixed:-0.1103\n",
      "\n",
      "epoch:25\n",
      "Time spent is 936.73011302948\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 26\n",
      "global iter 3250\n",
      "IZY:2.53 IZX:0.01\n",
      "acc:0.6562 avg_acc:0.6719\n",
      "acc_fixed:0.4375 avg_acc_fixed:0.4375\n",
      "vmi:-0.0976 avg_vmi:-0.0980\n",
      "vmi_fixed:-0.1103 avg_vmi_fixed:-0.1103\n",
      "\n",
      "epoch:26\n",
      "Time spent is 976.6758508682251\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 27\n",
      "global iter 3375\n",
      "IZY:2.53 IZX:0.01\n",
      "acc:0.7031 avg_acc:0.6250\n",
      "acc_fixed:0.5156 avg_acc_fixed:0.5156\n",
      "vmi:-0.0965 avg_vmi:-0.0973\n",
      "vmi_fixed:-0.1104 avg_vmi_fixed:-0.1104\n",
      "\n",
      "epoch:27\n",
      "Time spent is 1016.0992732048035\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 28\n",
      "global iter 3500\n",
      "IZY:2.53 IZX:0.01\n",
      "acc:0.5938 avg_acc:0.6406\n",
      "acc_fixed:0.5156 avg_acc_fixed:0.5156\n",
      "vmi:-0.0962 avg_vmi:-0.0958\n",
      "vmi_fixed:-0.1108 avg_vmi_fixed:-0.1108\n",
      "\n",
      "epoch:28\n",
      "Time spent is 1056.7078025341034\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 29\n",
      "global iter 3625\n",
      "IZY:2.54 IZX:0.01\n",
      "acc:0.6719 avg_acc:0.6250\n",
      "acc_fixed:0.5000 avg_acc_fixed:0.5000\n",
      "vmi:-0.0926 avg_vmi:-0.0931\n",
      "vmi_fixed:-0.1114 avg_vmi_fixed:-0.1114\n",
      "\n",
      "epoch:29\n",
      "Time spent is 1090.91854929924\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 30\n",
      "global iter 3750\n",
      "IZY:2.54 IZX:0.01\n",
      "acc:0.6250 avg_acc:0.5938\n",
      "acc_fixed:0.4688 avg_acc_fixed:0.4688\n",
      "vmi:-0.0911 avg_vmi:-0.0947\n",
      "vmi_fixed:-0.1119 avg_vmi_fixed:-0.1119\n",
      "\n",
      "epoch:30\n",
      "Time spent is 1123.9176931381226\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 31\n",
      "global iter 3875\n",
      "IZY:2.54 IZX:0.01\n",
      "acc:0.6406 avg_acc:0.6562\n",
      "acc_fixed:0.5156 avg_acc_fixed:0.5156\n",
      "vmi:-0.0917 avg_vmi:-0.0903\n",
      "vmi_fixed:-0.1122 avg_vmi_fixed:-0.1122\n",
      "\n",
      "epoch:31\n",
      "Time spent is 1156.0101566314697\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 32 Time since 20m 3s\n",
      "global iter 4000\n",
      "i:125 IZY:2.56 IZX:0.00\n",
      "acc:0.6250 avg_acc:0.7500\n",
      "acc_fixed:0.5000 avg_acc_fixed:0.5000\n",
      "vmi:0.0348 avg_vmi:0.0476\n",
      "vmi_fixed:-0.0037 avg_vmi_fixed:-0.0143\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 32\n",
      "global iter 4000\n",
      "IZY:2.54 IZX:0.01\n",
      "acc:0.6406 avg_acc:0.5938\n",
      "acc_fixed:0.5156 avg_acc_fixed:0.5156\n",
      "vmi:-0.0877 avg_vmi:-0.0896\n",
      "vmi_fixed:-0.1127 avg_vmi_fixed:-0.1127\n",
      "\n",
      "epoch:32\n",
      "Time spent is 1194.2665691375732\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 33\n",
      "global iter 4125\n",
      "IZY:2.54 IZX:0.01\n",
      "acc:0.6250 avg_acc:0.6250\n",
      "acc_fixed:0.5156 avg_acc_fixed:0.5156\n",
      "vmi:-0.0881 avg_vmi:-0.0869\n",
      "vmi_fixed:-0.1137 avg_vmi_fixed:-0.1137\n",
      "\n",
      "epoch:33\n",
      "Time spent is 1226.021208524704\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 34\n",
      "global iter 4250\n",
      "IZY:2.55 IZX:0.01\n",
      "acc:0.6719 avg_acc:0.6250\n",
      "acc_fixed:0.5156 avg_acc_fixed:0.5156\n",
      "vmi:-0.0849 avg_vmi:-0.0870\n",
      "vmi_fixed:-0.1151 avg_vmi_fixed:-0.1151\n",
      "\n",
      "epoch:34\n",
      "Time spent is 1258.0748038291931\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 35\n",
      "global iter 4375\n",
      "IZY:2.55 IZX:0.01\n",
      "acc:0.6719 avg_acc:0.6406\n",
      "acc_fixed:0.5156 avg_acc_fixed:0.5156\n",
      "vmi:-0.0818 avg_vmi:-0.0813\n",
      "vmi_fixed:-0.1166 avg_vmi_fixed:-0.1166\n",
      "\n",
      "epoch:35\n",
      "Time spent is 1289.651463508606\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 36\n",
      "global iter 4500\n",
      "IZY:2.55 IZX:0.01\n",
      "acc:0.6562 avg_acc:0.6562\n",
      "acc_fixed:0.5000 avg_acc_fixed:0.5000\n",
      "vmi:-0.0794 avg_vmi:-0.0818\n",
      "vmi_fixed:-0.1192 avg_vmi_fixed:-0.1192\n",
      "\n",
      "epoch:36\n",
      "Time spent is 1320.840808391571\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 37\n",
      "global iter 4625\n",
      "IZY:2.55 IZX:0.01\n",
      "acc:0.6094 avg_acc:0.6406\n",
      "acc_fixed:0.4688 avg_acc_fixed:0.4688\n",
      "vmi:-0.0815 avg_vmi:-0.0792\n",
      "vmi_fixed:-0.1219 avg_vmi_fixed:-0.1219\n",
      "\n",
      "epoch:37\n",
      "Time spent is 1352.1412580013275\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 38\n",
      "global iter 4750\n",
      "IZY:2.56 IZX:0.01\n",
      "acc:0.7031 avg_acc:0.6562\n",
      "acc_fixed:0.4844 avg_acc_fixed:0.4844\n",
      "vmi:-0.0700 avg_vmi:-0.0759\n",
      "vmi_fixed:-0.1244 avg_vmi_fixed:-0.1244\n",
      "\n",
      "epoch:38\n",
      "Time spent is 1383.7521996498108\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 39\n",
      "global iter 4875\n",
      "IZY:2.56 IZX:0.01\n",
      "acc:0.6562 avg_acc:0.6562\n",
      "acc_fixed:0.4531 avg_acc_fixed:0.4531\n",
      "vmi:-0.0728 avg_vmi:-0.0691\n",
      "vmi_fixed:-0.1272 avg_vmi_fixed:-0.1272\n",
      "\n",
      "epoch:39\n",
      "Time spent is 1416.1086785793304\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 40 Time since 24m 19s\n",
      "global iter 5000\n",
      "i:125 IZY:2.56 IZX:0.00\n",
      "acc:0.7500 avg_acc:0.6250\n",
      "acc_fixed:0.2500 avg_acc_fixed:0.1250\n",
      "vmi:-0.0360 avg_vmi:-0.0535\n",
      "vmi_fixed:-0.1338 avg_vmi_fixed:-0.1259\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 40\n",
      "global iter 5000\n",
      "IZY:2.56 IZX:0.01\n",
      "acc:0.6406 avg_acc:0.6719\n",
      "acc_fixed:0.4531 avg_acc_fixed:0.4531\n",
      "vmi:-0.0740 avg_vmi:-0.0696\n",
      "vmi_fixed:-0.1279 avg_vmi_fixed:-0.1279\n",
      "\n",
      "epoch:40\n",
      "Time spent is 1450.1381447315216\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 41\n",
      "global iter 5125\n",
      "IZY:2.57 IZX:0.00\n",
      "acc:0.6406 avg_acc:0.6094\n",
      "acc_fixed:0.4688 avg_acc_fixed:0.4688\n",
      "vmi:-0.0660 avg_vmi:-0.0681\n",
      "vmi_fixed:-0.1303 avg_vmi_fixed:-0.1303\n",
      "\n",
      "epoch:41\n",
      "Time spent is 1483.8692519664764\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 42\n",
      "global iter 5250\n",
      "IZY:2.58 IZX:0.00\n",
      "acc:0.6406 avg_acc:0.6406\n",
      "acc_fixed:0.4531 avg_acc_fixed:0.4531\n",
      "vmi:-0.0567 avg_vmi:-0.0650\n",
      "vmi_fixed:-0.1331 avg_vmi_fixed:-0.1331\n",
      "\n",
      "epoch:42\n",
      "Time spent is 1516.0602819919586\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 43\n",
      "global iter 5375\n",
      "IZY:2.57 IZX:0.00\n",
      "acc:0.6406 avg_acc:0.6406\n",
      "acc_fixed:0.4531 avg_acc_fixed:0.4531\n",
      "vmi:-0.0627 avg_vmi:-0.0621\n",
      "vmi_fixed:-0.1374 avg_vmi_fixed:-0.1374\n",
      "\n",
      "epoch:43\n",
      "Time spent is 1550.0108532905579\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 44\n",
      "global iter 5500\n",
      "IZY:2.58 IZX:0.00\n",
      "acc:0.7031 avg_acc:0.6406\n",
      "acc_fixed:0.4531 avg_acc_fixed:0.4531\n",
      "vmi:-0.0527 avg_vmi:-0.0579\n",
      "vmi_fixed:-0.1410 avg_vmi_fixed:-0.1410\n",
      "\n",
      "epoch:44\n",
      "Time spent is 1583.2812535762787\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 45\n",
      "global iter 5625\n",
      "IZY:2.57 IZX:0.00\n",
      "acc:0.6250 avg_acc:0.6562\n",
      "acc_fixed:0.4375 avg_acc_fixed:0.4375\n",
      "vmi:-0.0601 avg_vmi:-0.0544\n",
      "vmi_fixed:-0.1453 avg_vmi_fixed:-0.1453\n",
      "\n",
      "epoch:45\n",
      "Time spent is 1616.288626909256\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 46\n",
      "global iter 5750\n",
      "IZY:2.58 IZX:0.00\n",
      "acc:0.6719 avg_acc:0.6250\n",
      "acc_fixed:0.4531 avg_acc_fixed:0.4531\n",
      "vmi:-0.0539 avg_vmi:-0.0569\n",
      "vmi_fixed:-0.1490 avg_vmi_fixed:-0.1490\n",
      "\n",
      "epoch:46\n",
      "Time spent is 1649.5750617980957\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 47\n",
      "global iter 5875\n",
      "IZY:2.59 IZX:0.00\n",
      "acc:0.6875 avg_acc:0.6562\n",
      "acc_fixed:0.3906 avg_acc_fixed:0.3906\n",
      "vmi:-0.0426 avg_vmi:-0.0468\n",
      "vmi_fixed:-0.1555 avg_vmi_fixed:-0.1555\n",
      "\n",
      "epoch:47\n",
      "Time spent is 1684.265463590622\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 48 Time since 28m 47s\n",
      "global iter 6000\n",
      "i:125 IZY:2.72 IZX:0.00\n",
      "acc:1.0000 avg_acc:1.0000\n",
      "acc_fixed:0.2500 avg_acc_fixed:0.2500\n",
      "vmi:0.1717 avg_vmi:0.1427\n",
      "vmi_fixed:-0.1612 avg_vmi_fixed:-0.1017\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 48\n",
      "global iter 6000\n",
      "IZY:2.58 IZX:0.00\n",
      "acc:0.5938 avg_acc:0.6719\n",
      "acc_fixed:0.4062 avg_acc_fixed:0.4062\n",
      "vmi:-0.0541 avg_vmi:-0.0403\n",
      "vmi_fixed:-0.1626 avg_vmi_fixed:-0.1626\n",
      "\n",
      "epoch:48\n",
      "Time spent is 1718.2900812625885\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 49\n",
      "global iter 6125\n",
      "IZY:2.59 IZX:0.00\n",
      "acc:0.6562 avg_acc:0.6250\n",
      "acc_fixed:0.3906 avg_acc_fixed:0.3906\n",
      "vmi:-0.0464 avg_vmi:-0.0493\n",
      "vmi_fixed:-0.1690 avg_vmi_fixed:-0.1690\n",
      "\n",
      "epoch:49\n",
      "Time spent is 1751.9133944511414\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 50\n",
      "global iter 6250\n",
      "IZY:2.56 IZX:0.00\n",
      "acc:0.6094 avg_acc:0.6562\n",
      "acc_fixed:0.3594 avg_acc_fixed:0.3594\n",
      "vmi:-0.0702 avg_vmi:-0.0419\n",
      "vmi_fixed:-0.1734 avg_vmi_fixed:-0.1734\n",
      "\n",
      "epoch:50\n",
      "Time spent is 1785.5715346336365\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 51\n",
      "global iter 6375\n",
      "IZY:2.58 IZX:0.00\n",
      "acc:0.6406 avg_acc:0.7031\n",
      "acc_fixed:0.3281 avg_acc_fixed:0.3281\n",
      "vmi:-0.0536 avg_vmi:-0.0345\n",
      "vmi_fixed:-0.1799 avg_vmi_fixed:-0.1799\n",
      "\n",
      "epoch:51\n",
      "Time spent is 1819.631287097931\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 52\n",
      "global iter 6500\n",
      "IZY:2.59 IZX:0.00\n",
      "acc:0.6719 avg_acc:0.6562\n",
      "acc_fixed:0.3125 avg_acc_fixed:0.3125\n",
      "vmi:-0.0406 avg_vmi:-0.0431\n",
      "vmi_fixed:-0.1832 avg_vmi_fixed:-0.1832\n",
      "\n",
      "epoch:52\n",
      "Time spent is 1853.567632675171\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 53\n",
      "global iter 6625\n",
      "IZY:2.59 IZX:0.00\n",
      "acc:0.6719 avg_acc:0.6406\n",
      "acc_fixed:0.2969 avg_acc_fixed:0.2969\n",
      "vmi:-0.0423 avg_vmi:-0.0414\n",
      "vmi_fixed:-0.1886 avg_vmi_fixed:-0.1886\n",
      "\n",
      "epoch:53\n",
      "Time spent is 1886.899569272995\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 54\n",
      "global iter 6750\n",
      "IZY:2.58 IZX:0.00\n",
      "acc:0.6250 avg_acc:0.6562\n",
      "acc_fixed:0.3281 avg_acc_fixed:0.3281\n",
      "vmi:-0.0495 avg_vmi:-0.0344\n",
      "vmi_fixed:-0.1930 avg_vmi_fixed:-0.1930\n",
      "\n",
      "epoch:54\n",
      "Time spent is 1921.8090348243713\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 55\n",
      "global iter 6875\n",
      "IZY:2.59 IZX:0.00\n",
      "acc:0.7031 avg_acc:0.6406\n",
      "acc_fixed:0.3438 avg_acc_fixed:0.3438\n",
      "vmi:-0.0451 avg_vmi:-0.0342\n",
      "vmi_fixed:-0.1967 avg_vmi_fixed:-0.1967\n",
      "\n",
      "epoch:55\n",
      "Time spent is 1955.8291091918945\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 56 Time since 33m 18s\n",
      "global iter 7000\n",
      "i:125 IZY:2.58 IZX:0.00\n",
      "acc:0.6250 avg_acc:0.7500\n",
      "acc_fixed:0.3750 avg_acc_fixed:0.3750\n",
      "vmi:0.0163 avg_vmi:-0.0053\n",
      "vmi_fixed:-0.2144 avg_vmi_fixed:-0.2157\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 56\n",
      "global iter 7000\n",
      "IZY:2.60 IZX:0.00\n",
      "acc:0.6250 avg_acc:0.6250\n",
      "acc_fixed:0.3438 avg_acc_fixed:0.3438\n",
      "vmi:-0.0360 avg_vmi:-0.0312\n",
      "vmi_fixed:-0.1994 avg_vmi_fixed:-0.1994\n",
      "\n",
      "epoch:56\n",
      "Time spent is 1988.133008480072\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 57\n",
      "global iter 7125\n",
      "IZY:2.57 IZX:0.00\n",
      "acc:0.6250 avg_acc:0.6875\n",
      "acc_fixed:0.3438 avg_acc_fixed:0.3438\n",
      "vmi:-0.0545 avg_vmi:-0.0289\n",
      "vmi_fixed:-0.2022 avg_vmi_fixed:-0.2022\n",
      "\n",
      "epoch:57\n",
      "Time spent is 2022.3927874565125\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 58\n",
      "global iter 7250\n",
      "IZY:2.63 IZX:0.00\n",
      "acc:0.7344 avg_acc:0.6562\n",
      "acc_fixed:0.3281 avg_acc_fixed:0.3281\n",
      "vmi:-0.0111 avg_vmi:-0.0313\n",
      "vmi_fixed:-0.2097 avg_vmi_fixed:-0.2097\n",
      "\n",
      "epoch:58\n",
      "Time spent is 2054.1876554489136\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 59\n",
      "global iter 7375\n",
      "IZY:2.61 IZX:0.00\n",
      "acc:0.6250 avg_acc:0.6562\n",
      "acc_fixed:0.3281 avg_acc_fixed:0.3281\n",
      "vmi:-0.0275 avg_vmi:-0.0243\n",
      "vmi_fixed:-0.2123 avg_vmi_fixed:-0.2123\n",
      "\n",
      "epoch:59\n",
      "Time spent is 2088.4801952838898\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 60\n",
      "global iter 7500\n",
      "IZY:2.64 IZX:0.00\n",
      "acc:0.6875 avg_acc:0.6719\n",
      "acc_fixed:0.3438 avg_acc_fixed:0.3438\n",
      "vmi:0.0009 avg_vmi:-0.0238\n",
      "vmi_fixed:-0.2205 avg_vmi_fixed:-0.2205\n",
      "\n",
      "epoch:60\n",
      "Time spent is 2121.4342999458313\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 61\n",
      "global iter 7625\n",
      "IZY:2.62 IZX:0.00\n",
      "acc:0.6875 avg_acc:0.6562\n",
      "acc_fixed:0.3438 avg_acc_fixed:0.3438\n",
      "vmi:-0.0132 avg_vmi:-0.0172\n",
      "vmi_fixed:-0.2258 avg_vmi_fixed:-0.2258\n",
      "\n",
      "epoch:61\n",
      "Time spent is 2155.629629611969\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 62\n",
      "global iter 7750\n",
      "IZY:2.63 IZX:0.00\n",
      "acc:0.7188 avg_acc:0.6875\n",
      "acc_fixed:0.3281 avg_acc_fixed:0.3281\n",
      "vmi:-0.0049 avg_vmi:-0.0200\n",
      "vmi_fixed:-0.2318 avg_vmi_fixed:-0.2318\n",
      "\n",
      "epoch:62\n",
      "Time spent is 2189.463306903839\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 63\n",
      "global iter 7875\n",
      "IZY:2.60 IZX:0.00\n",
      "acc:0.6719 avg_acc:0.6406\n",
      "acc_fixed:0.3281 avg_acc_fixed:0.3281\n",
      "vmi:-0.0314 avg_vmi:-0.0378\n",
      "vmi_fixed:-0.2305 avg_vmi_fixed:-0.2305\n",
      "\n",
      "epoch:63\n",
      "Time spent is 2222.720778942108\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 64 Time since 37m 45s\n",
      "global iter 8000\n",
      "i:125 IZY:2.66 IZX:0.00\n",
      "acc:0.6250 avg_acc:0.7500\n",
      "acc_fixed:0.2500 avg_acc_fixed:0.2500\n",
      "vmi:0.1174 avg_vmi:0.1208\n",
      "vmi_fixed:-0.3024 avg_vmi_fixed:-0.1866\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 64\n",
      "global iter 8000\n",
      "IZY:2.60 IZX:0.00\n",
      "acc:0.6406 avg_acc:0.6875\n",
      "acc_fixed:0.3125 avg_acc_fixed:0.3125\n",
      "vmi:-0.0336 avg_vmi:-0.0170\n",
      "vmi_fixed:-0.2339 avg_vmi_fixed:-0.2339\n",
      "\n",
      "epoch:64\n",
      "Time spent is 2255.7737860679626\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 65\n",
      "global iter 8125\n",
      "IZY:2.61 IZX:0.00\n",
      "acc:0.6719 avg_acc:0.6719\n",
      "acc_fixed:0.2969 avg_acc_fixed:0.2969\n",
      "vmi:-0.0208 avg_vmi:-0.0219\n",
      "vmi_fixed:-0.2387 avg_vmi_fixed:-0.2387\n",
      "\n",
      "epoch:65\n",
      "Time spent is 2291.5834136009216\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 66\n",
      "global iter 8250\n",
      "IZY:2.62 IZX:0.00\n",
      "acc:0.6406 avg_acc:0.6562\n",
      "acc_fixed:0.2969 avg_acc_fixed:0.2969\n",
      "vmi:-0.0256 avg_vmi:-0.0199\n",
      "vmi_fixed:-0.2386 avg_vmi_fixed:-0.2386\n",
      "\n",
      "epoch:66\n",
      "Time spent is 2326.608375310898\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 67\n",
      "global iter 8375\n",
      "IZY:2.63 IZX:0.00\n",
      "acc:0.6406 avg_acc:0.6719\n",
      "acc_fixed:0.3281 avg_acc_fixed:0.3281\n",
      "vmi:-0.0010 avg_vmi:-0.0179\n",
      "vmi_fixed:-0.2404 avg_vmi_fixed:-0.2404\n",
      "\n",
      "epoch:67\n",
      "Time spent is 2361.0553002357483\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 68\n",
      "global iter 8500\n",
      "IZY:2.64 IZX:0.00\n",
      "acc:0.7344 avg_acc:0.6719\n",
      "acc_fixed:0.2969 avg_acc_fixed:0.2969\n",
      "vmi:-0.0074 avg_vmi:-0.0115\n",
      "vmi_fixed:-0.2428 avg_vmi_fixed:-0.2428\n",
      "\n",
      "epoch:68\n",
      "Time spent is 2396.947431087494\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 69\n",
      "global iter 8625\n",
      "IZY:2.62 IZX:0.00\n",
      "acc:0.6406 avg_acc:0.7188\n",
      "acc_fixed:0.2969 avg_acc_fixed:0.2969\n",
      "vmi:-0.0199 avg_vmi:-0.0036\n",
      "vmi_fixed:-0.2441 avg_vmi_fixed:-0.2441\n",
      "\n",
      "epoch:69\n",
      "Time spent is 2432.398980617523\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 70\n",
      "global iter 8750\n",
      "IZY:2.63 IZX:0.00\n",
      "acc:0.6406 avg_acc:0.6562\n",
      "acc_fixed:0.3125 avg_acc_fixed:0.3125\n",
      "vmi:-0.0102 avg_vmi:-0.0245\n",
      "vmi_fixed:-0.2461 avg_vmi_fixed:-0.2461\n",
      "\n",
      "epoch:70\n",
      "Time spent is 2466.547901391983\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 71\n",
      "global iter 8875\n",
      "IZY:2.64 IZX:0.00\n",
      "acc:0.7031 avg_acc:0.6719\n",
      "acc_fixed:0.2656 avg_acc_fixed:0.2656\n",
      "vmi:-0.0017 avg_vmi:-0.0045\n",
      "vmi_fixed:-0.2495 avg_vmi_fixed:-0.2495\n",
      "\n",
      "epoch:71\n",
      "Time spent is 2500.6654183864594\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 72 Time since 42m 26s\n",
      "global iter 9000\n",
      "i:125 IZY:2.67 IZX:0.00\n",
      "acc:0.7500 avg_acc:0.8750\n",
      "acc_fixed:0.2500 avg_acc_fixed:0.3750\n",
      "vmi:-0.0004 avg_vmi:0.0924\n",
      "vmi_fixed:-0.6407 avg_vmi_fixed:-0.6033\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 72\n",
      "global iter 9000\n",
      "IZY:2.65 IZX:0.00\n",
      "acc:0.7031 avg_acc:0.6875\n",
      "acc_fixed:0.2656 avg_acc_fixed:0.2656\n",
      "vmi:0.0065 avg_vmi:-0.0081\n",
      "vmi_fixed:-0.2554 avg_vmi_fixed:-0.2554\n",
      "\n",
      "epoch:72\n",
      "Time spent is 2536.5261607170105\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 73\n",
      "global iter 9125\n",
      "IZY:2.62 IZX:0.00\n",
      "acc:0.7188 avg_acc:0.6875\n",
      "acc_fixed:0.3125 avg_acc_fixed:0.3125\n",
      "vmi:-0.0189 avg_vmi:-0.0017\n",
      "vmi_fixed:-0.2593 avg_vmi_fixed:-0.2593\n",
      "\n",
      "epoch:73\n",
      "Time spent is 2571.7546586990356\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 74\n",
      "global iter 9250\n",
      "IZY:2.64 IZX:0.00\n",
      "acc:0.6875 avg_acc:0.6250\n",
      "acc_fixed:0.2969 avg_acc_fixed:0.2969\n",
      "vmi:-0.0056 avg_vmi:-0.0214\n",
      "vmi_fixed:-0.2606 avg_vmi_fixed:-0.2606\n",
      "\n",
      "epoch:74\n",
      "Time spent is 2606.1861760616302\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 75\n",
      "global iter 9375\n",
      "IZY:2.66 IZX:0.00\n",
      "acc:0.7031 avg_acc:0.6719\n",
      "acc_fixed:0.2812 avg_acc_fixed:0.2812\n",
      "vmi:0.0098 avg_vmi:-0.0074\n",
      "vmi_fixed:-0.2653 avg_vmi_fixed:-0.2653\n",
      "\n",
      "epoch:75\n",
      "Time spent is 2642.2099163532257\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 76\n",
      "global iter 9500\n",
      "IZY:2.63 IZX:0.00\n",
      "acc:0.6562 avg_acc:0.6875\n",
      "acc_fixed:0.2812 avg_acc_fixed:0.2812\n",
      "vmi:-0.0101 avg_vmi:-0.0176\n",
      "vmi_fixed:-0.2668 avg_vmi_fixed:-0.2668\n",
      "\n",
      "epoch:76\n",
      "Time spent is 2677.4392924308777\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 77\n",
      "global iter 9625\n",
      "IZY:2.66 IZX:0.00\n",
      "acc:0.7188 avg_acc:0.7031\n",
      "acc_fixed:0.2812 avg_acc_fixed:0.2812\n",
      "vmi:0.0146 avg_vmi:-0.0029\n",
      "vmi_fixed:-0.2631 avg_vmi_fixed:-0.2631\n",
      "\n",
      "epoch:77\n",
      "Time spent is 2711.1124205589294\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 78\n",
      "global iter 9750\n",
      "IZY:2.63 IZX:0.00\n",
      "acc:0.6406 avg_acc:0.6406\n",
      "acc_fixed:0.3125 avg_acc_fixed:0.3125\n",
      "vmi:-0.0106 avg_vmi:-0.0179\n",
      "vmi_fixed:-0.2580 avg_vmi_fixed:-0.2580\n",
      "\n",
      "epoch:78\n",
      "Time spent is 2742.6079812049866\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 79\n",
      "global iter 9875\n",
      "IZY:2.68 IZX:0.00\n",
      "acc:0.7500 avg_acc:0.7031\n",
      "acc_fixed:0.2969 avg_acc_fixed:0.2969\n",
      "vmi:0.0332 avg_vmi:-0.0025\n",
      "vmi_fixed:-0.2602 avg_vmi_fixed:-0.2602\n",
      "\n",
      "epoch:79\n",
      "Time spent is 2773.0442070961\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 80 Time since 46m 56s\n",
      "global iter 10000\n",
      "i:125 IZY:2.70 IZX:0.00\n",
      "acc:0.7500 avg_acc:1.0000\n",
      "acc_fixed:0.5000 avg_acc_fixed:0.3750\n",
      "vmi:0.1187 avg_vmi:0.1659\n",
      "vmi_fixed:-0.0833 avg_vmi_fixed:-0.0892\n",
      "test True\n",
      "tensor([[-0.1922, -0.1843, -0.1608,  ..., -0.2392, -0.2392, -0.2314],\n",
      "        [-0.2627, -0.2627, -0.2471,  ..., -0.2157, -0.2235, -0.2235],\n",
      "        [-0.3020, -0.3098, -0.3098,  ..., -0.1686, -0.1922, -0.2000],\n",
      "        ...,\n",
      "        [-0.9294, -0.9294, -0.9451,  ..., -0.8039, -0.8039, -0.7961],\n",
      "        [-0.8431, -0.8667, -0.8902,  ..., -0.8039, -0.7961, -0.7882],\n",
      "        [-0.8039, -0.8039, -0.8039,  ..., -0.8275, -0.8353, -0.8353]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9922, -0.9922, -0.9922,  ..., -0.9529, -0.1765, -0.9922],\n",
      "        [-0.9922, -0.9922, -0.9922,  ..., -0.9529, -0.1765, -0.9843],\n",
      "        [-0.9922, -0.9922, -0.9922,  ..., -0.9608, -0.1765, -0.9922],\n",
      "        ...,\n",
      "        [-0.5922, -0.6000, -0.5608,  ..., -0.7882, -0.8196, -0.8039],\n",
      "        [-0.6000, -0.5608, -0.5529,  ..., -0.8039, -0.7647, -0.7725],\n",
      "        [-0.5294, -0.5922, -0.5922,  ..., -0.7647, -0.7961, -0.8431]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.7961, -0.8745,  0.4824,  ...,  0.6314,  0.7098,  0.7569],\n",
      "        [-0.9373, -0.9686,  0.1686,  ...,  0.6000,  0.6078,  0.6157],\n",
      "        [-0.9922, -0.9843,  0.2627,  ...,  0.3569,  0.3961,  0.3647],\n",
      "        ...,\n",
      "        [-0.7725, -0.7961, -0.8118,  ..., -0.8039, -0.8196, -0.8039],\n",
      "        [-0.7961, -0.7647, -0.7647,  ..., -0.8196, -0.8039, -0.7490],\n",
      "        [-0.8039, -0.7647, -0.7490,  ..., -0.8275, -0.8118, -0.7725]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9922, -0.9922, -0.9922,  ...,  0.5059,  0.4118, -1.0000],\n",
      "        [-0.9922, -0.9922, -0.9922,  ...,  0.3412,  0.2941,  0.2235],\n",
      "        [-0.9922, -0.9922, -0.9922,  ...,  0.3176,  0.3098,  0.2863],\n",
      "        ...,\n",
      "        [-0.7569, -0.7176, -0.7020,  ..., -0.8353, -0.8588, -0.8745],\n",
      "        [-0.7490, -0.7804, -0.7647,  ..., -0.8431, -0.8353, -0.8275],\n",
      "        [-0.7569, -0.7647, -0.7490,  ..., -0.8353, -0.8431, -0.8902]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.7020,  0.4353,  0.5765,  ...,  0.3098,  0.3412,  0.2549],\n",
      "        [ 0.4039, -0.6863, -0.9529,  ...,  0.0353,  0.0667,  0.1137],\n",
      "        [ 0.5765, -0.9608, -0.9294,  ...,  0.2078,  0.2000,  0.2000],\n",
      "        ...,\n",
      "        [-0.3725, -0.3255, -0.2627,  ...,  0.0745,  0.0431,  0.0980],\n",
      "        [-0.3098, -0.2941, -0.2392,  ..., -0.0353, -0.0118,  0.0431],\n",
      "        [-0.2627, -0.3020, -0.2784,  ..., -0.0275,  0.0510,  0.0588]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.5216, -0.8431, -0.5451,  ...,  0.7882,  0.7647,  0.8118],\n",
      "        [-0.4118, -0.8118,  0.4196,  ...,  0.7020,  0.7020,  0.7255],\n",
      "        [-0.3804, -0.7333, -0.4196,  ...,  0.6863,  0.7255,  0.7333],\n",
      "        ...,\n",
      "        [-0.8510, -0.8431, -0.8353,  ..., -0.8588, -0.8588, -0.8588],\n",
      "        [-0.8510, -0.8275, -0.8196,  ..., -0.8510, -0.8431, -0.8196],\n",
      "        [-0.8353, -0.8902, -0.8980,  ..., -0.8118, -0.8353, -0.8118]],\n",
      "       device='cuda:0')\n",
      "tensor([[ 0.2549,  0.5529,  0.6471,  ...,  0.3333, -0.7569, -0.9922],\n",
      "        [ 0.0196, -0.0431,  0.0039,  ..., -0.0118, -0.4118, -0.9922],\n",
      "        [-0.0275,  0.0118,  0.1059,  ..., -0.1294, -0.2471, -0.9922],\n",
      "        ...,\n",
      "        [ 0.0588,  0.0431,  0.0902,  ..., -0.2627, -0.3333, -0.3569],\n",
      "        [ 0.0902,  0.1686,  0.0980,  ..., -0.1765, -0.2078, -0.2078],\n",
      "        [ 0.0275,  0.1137,  0.2000,  ...,  0.1059,  0.1608,  0.1451]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9608,  0.6157, -0.6549,  ...,  0.6627,  0.6941,  0.7020],\n",
      "        [-0.9294, -0.3647,  0.7804,  ...,  0.6078,  0.6000,  0.6000],\n",
      "        [-0.9608, -0.2549, -0.9451,  ...,  0.5608,  0.4745,  0.4510],\n",
      "        ...,\n",
      "        [-0.6627, -0.7020, -0.7176,  ..., -0.8745, -0.8667, -0.8353],\n",
      "        [-0.7098, -0.7020, -0.6784,  ..., -0.9216, -0.9059, -0.8980],\n",
      "        [-0.7490, -0.7490, -0.7412,  ..., -0.8588, -0.8353, -0.8353]],\n",
      "       device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-0.4353, -0.1137, -0.6157,  ...,  0.4118,  0.3569, -1.0000],\n",
      "        [ 0.3725, -0.4275,  0.5529,  ...,  0.2157,  0.3098,  0.0431],\n",
      "        [-0.7725, -0.2157, -0.3333,  ...,  0.2863,  0.2235,  0.2627],\n",
      "        ...,\n",
      "        [-0.3804, -0.3333, -0.2706,  ..., -0.6078, -0.6314, -0.6078],\n",
      "        [-0.3255, -0.3412, -0.3098,  ..., -0.6314, -0.6549, -0.6627],\n",
      "        [-0.3412, -0.3098, -0.2706,  ..., -0.6392, -0.6706, -0.6627]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.2078, -0.7725,  0.2941,  ...,  0.7412,  0.5765,  0.5059],\n",
      "        [-0.9922, -0.8745,  0.5765,  ...,  0.5294,  0.4039,  0.3804],\n",
      "        [-0.3804, -0.5608,  0.4275,  ...,  0.1922,  0.1373,  0.1608],\n",
      "        ...,\n",
      "        [-0.7725, -0.7882, -0.8353,  ..., -0.8353, -0.8196, -0.8039],\n",
      "        [-0.7882, -0.7725, -0.8275,  ..., -0.8118, -0.7882, -0.7961],\n",
      "        [-0.7725, -0.7882, -0.8275,  ..., -0.8196, -0.8353, -0.8353]],\n",
      "       device='cuda:0')\n",
      "tensor([[ 0.4902, -1.0000, -0.7412,  ...,  0.5216,  0.4510,  0.3882],\n",
      "        [ 0.5843, -0.9765, -0.9843,  ...,  0.3569,  0.3255,  0.3647],\n",
      "        [ 0.5059, -0.9843, -0.3961,  ...,  0.4275,  0.3569,  0.3490],\n",
      "        ...,\n",
      "        [-0.8196, -0.7882, -0.8039,  ..., -0.7804, -0.7412, -0.7647],\n",
      "        [-0.8510, -0.8510, -0.8275,  ..., -0.8039, -0.8275, -0.8196],\n",
      "        [-0.7961, -0.7725, -0.7882,  ..., -0.8275, -0.8275, -0.8353]],\n",
      "       device='cuda:0')\n",
      "tensor([[ 0.1843, -0.6706, -0.2078,  ...,  0.8902,  0.8824,  0.6157],\n",
      "        [ 0.2078, -0.6863,  0.5529,  ...,  0.8275,  0.8196,  0.7176],\n",
      "        [ 0.1686, -0.6549,  0.4745,  ...,  0.5529,  0.5451,  0.5059],\n",
      "        ...,\n",
      "        [-0.7333, -0.7412, -0.7255,  ..., -0.9373, -0.9137, -0.9059],\n",
      "        [-0.8353, -0.8118, -0.8431,  ..., -0.9686, -0.9686, -0.9686],\n",
      "        [-0.9922, -1.0000, -0.9843,  ..., -0.9922, -0.9765, -0.9686]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.2471,  0.4980, -0.9294,  ...,  0.3647,  0.4118,  0.3647],\n",
      "        [-0.7882,  0.0588, -0.5765,  ...,  0.2078,  0.2000,  0.0980],\n",
      "        [-0.9608, -0.2863, -0.3647,  ...,  0.0824, -0.0118, -0.1216],\n",
      "        ...,\n",
      "        [-0.8275, -0.8275, -0.8353,  ..., -0.8588, -0.8431, -0.8431],\n",
      "        [-0.8353, -0.8588, -0.8431,  ..., -0.8353, -0.8196, -0.7961],\n",
      "        [-0.8745, -0.8510, -0.8275,  ..., -0.8039, -0.8196, -0.7961]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9765, -0.2314, -0.1843,  ..., -0.1373, -0.2157, -0.2392],\n",
      "        [-1.0000,  0.1608,  0.3176,  ...,  0.1922,  0.1686,  0.1294],\n",
      "        [-0.7490, -0.3020, -0.2863,  ..., -0.3333, -0.3804, -0.4667],\n",
      "        ...,\n",
      "        [-0.8039, -0.7882, -0.7804,  ..., -0.7490, -0.7176, -0.7098],\n",
      "        [-0.7333, -0.7255, -0.7490,  ..., -0.6863, -0.6784, -0.6706],\n",
      "        [-0.7412, -0.7647, -0.7804,  ..., -0.7490, -0.7569, -0.7569]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9686, -0.3098, -0.0039,  ...,  0.2471,  0.1922,  0.1843],\n",
      "        [-0.7098, -0.8824,  0.5451,  ...,  0.2471,  0.2784,  0.2471],\n",
      "        [-0.9843, -0.8510,  0.5373,  ...,  0.1451,  0.1765,  0.1529],\n",
      "        ...,\n",
      "        [-0.7412, -0.8275, -0.8745,  ..., -0.9765, -0.9529, -0.9765],\n",
      "        [-0.7490, -0.8353, -0.8667,  ..., -0.9686, -0.9843, -1.0000],\n",
      "        [-0.7961, -0.8275, -0.8275,  ..., -0.9608, -0.9608, -0.9608]],\n",
      "       device='cuda:0')\n",
      "tensor([[ 0.1922, -0.3490,  0.2549,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        [-0.8980, -0.1765,  0.2941,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        [ 0.4902, -0.8902,  0.2706,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        ...,\n",
      "        [-0.8588, -0.8667, -0.8196,  ..., -0.5843, -0.5373, -0.5608],\n",
      "        [-0.8118, -0.8196, -0.8745,  ..., -0.5608, -0.5059, -0.4745],\n",
      "        [-0.8118, -0.8039, -0.8275,  ..., -0.6157, -0.6235, -0.6078]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9216,  0.4431, -0.8118,  ...,  0.0745,  0.1373, -0.0510],\n",
      "        [-0.8824, -0.1216, -0.5137,  ...,  0.2157,  0.2235,  0.1294],\n",
      "        [-0.9843, -0.1216, -0.5608,  ...,  0.0824,  0.1059,  0.1216],\n",
      "        ...,\n",
      "        [-0.8039, -0.8196, -0.8118,  ..., -0.8118, -0.8353, -0.8353],\n",
      "        [-0.8275, -0.8196, -0.7804,  ..., -0.8196, -0.8275, -0.8196],\n",
      "        [-0.7882, -0.8039, -0.8039,  ..., -0.8275, -0.8275, -0.8039]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.1686, -0.0353,  0.0667,  ...,  0.1922,  0.1686,  0.0510],\n",
      "        [-0.1059, -0.0667, -0.0510,  ...,  0.0275, -0.0824, -0.1922],\n",
      "        [-0.1922, -0.0196, -0.0275,  ..., -0.1059, -0.1529, -0.1216],\n",
      "        ...,\n",
      "        [-0.4588, -0.4431, -0.3647,  ..., -0.1451, -0.0824, -0.0118],\n",
      "        [-0.5216, -0.4980, -0.4667,  ..., -0.0980, -0.0039, -0.0039],\n",
      "        [-0.6078, -0.5765, -0.5294,  ..., -0.0902, -0.0902, -0.1294]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.3961, -0.4118, -0.4118,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        [-0.2863, -0.3490, -0.3804,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        [-0.2549, -0.2471, -0.2157,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        ...,\n",
      "        [-0.7725, -0.7804, -0.8118,  ..., -0.6941, -0.6784, -0.6392],\n",
      "        [-0.7804, -0.7569, -0.7804,  ..., -0.6235, -0.6627, -0.6392],\n",
      "        [-0.6941, -0.7490, -0.7882,  ..., -0.6314, -0.6157, -0.6157]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9843, -0.9608,  0.4667,  ...,  0.2706,  0.1529, -0.9922],\n",
      "        [-0.9843, -0.9843,  0.3333,  ...,  0.1373,  0.0118, -0.9765],\n",
      "        [-0.9843, -0.4118,  0.4980,  ...,  0.2235,  0.0667, -0.2392],\n",
      "        ...,\n",
      "        [-0.6941, -0.6706, -0.6549,  ..., -0.6392, -0.6314, -0.6471],\n",
      "        [-0.7569, -0.7098, -0.7098,  ..., -0.6392, -0.6784, -0.6863],\n",
      "        [-0.7569, -0.7490, -0.7333,  ..., -0.6706, -0.6863, -0.7020]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9529,  0.5294,  0.4431,  ...,  0.7412,  0.6784,  0.5843],\n",
      "        [-0.9451,  0.4431,  0.5373,  ...,  0.6784,  0.6627,  0.6000],\n",
      "        [-0.9373,  0.4588, -0.3725,  ...,  0.5529,  0.4902,  0.4196],\n",
      "        ...,\n",
      "        [-0.6157, -0.6314, -0.6235,  ..., -0.6784, -0.6549, -0.6157],\n",
      "        [-0.6157, -0.6706, -0.7176,  ..., -0.6627, -0.6549, -0.6157],\n",
      "        [-0.6549, -0.6627, -0.7020,  ..., -0.7098, -0.6863, -0.7020]],\n",
      "       device='cuda:0')\n",
      "tensor([[ 0.2314, -0.9922,  0.2627,  ...,  1.0000,  0.9608,  0.9216],\n",
      "        [-0.7961, -0.2471,  0.5843,  ...,  0.8275,  0.8745,  0.8353],\n",
      "        [-0.9216, -0.9608,  0.5608,  ...,  0.7020,  0.6706,  0.7098],\n",
      "        ...,\n",
      "        [-0.6392, -0.6784, -0.6863,  ..., -0.7804, -0.7569, -0.7569],\n",
      "        [-0.7333, -0.7176, -0.6627,  ..., -0.7020, -0.7176, -0.7333],\n",
      "        [-0.7490, -0.7333, -0.7098,  ..., -0.7569, -0.7020, -0.6941]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9922, -0.9765,  0.2784,  ...,  0.3569,  0.3333,  0.3725],\n",
      "        [-0.9843, -0.4431,  0.4510,  ...,  0.3961,  0.3647,  0.3255],\n",
      "        [ 0.1922,  0.5451,  0.4745,  ...,  0.3255,  0.3098,  0.2392],\n",
      "        ...,\n",
      "        [-0.8353, -0.8588, -0.8667,  ..., -0.8275, -0.7882, -0.8118],\n",
      "        [-0.8353, -0.7961, -0.7961,  ..., -0.7961, -0.8275, -0.8431],\n",
      "        [-0.7961, -0.8196, -0.8275,  ..., -0.7961, -0.8353, -0.8431]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9922, -0.9922, -0.0510,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        [ 0.4902, -0.3882, -0.0980,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        [-0.6235,  0.4667, -0.9765,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        ...,\n",
      "        [-0.8039, -0.8039, -0.8431,  ..., -0.7333, -0.7098, -0.6706],\n",
      "        [-0.8196, -0.7882, -0.7725,  ..., -0.6235, -0.6000, -0.6392],\n",
      "        [-0.8275, -0.8118, -0.7961,  ..., -0.6549, -0.7020, -0.7569]],\n",
      "       device='cuda:0')\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 80\n",
      "global iter 10000\n",
      "IZY:2.64 IZX:0.00\n",
      "acc:0.7031 avg_acc:0.6094\n",
      "acc_fixed:0.3125 avg_acc_fixed:0.3125\n",
      "vmi:-0.0088 avg_vmi:-0.0191\n",
      "vmi_fixed:-0.2577 avg_vmi_fixed:-0.2577\n",
      "\n",
      "epoch:80\n",
      "Time spent is 2809.703731775284\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 81\n",
      "global iter 10125\n",
      "IZY:2.68 IZX:0.00\n",
      "acc:0.7812 avg_acc:0.6875\n",
      "acc_fixed:0.3281 avg_acc_fixed:0.3281\n",
      "vmi:0.0259 avg_vmi:-0.0217\n",
      "vmi_fixed:-0.2671 avg_vmi_fixed:-0.2671\n",
      "\n",
      "epoch:81\n",
      "Time spent is 2845.9853014945984\n",
      "test True\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]='0'\n",
    "import numpy as np\n",
    "import torch\n",
    "import argparse\n",
    "from utils import str2bool\n",
    "from solver import Solver\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "\n",
    "parser = argparse.ArgumentParser(description='VIBI for interpretation')\n",
    "parser.add_argument('--epoch', default=100, type=int,\n",
    "                        help='epoch number')\n",
    "parser.add_argument('--lr', default=1e-6, type=float,\n",
    "                        help='learning rate')\n",
    "parser.add_argument('--beta', default=0.01, type=float,\n",
    "                        help='beta for balance between information loss and prediction loss')\n",
    "parser.add_argument('--K', default=100, type=int,\n",
    "                        help='dimension of encoding Z')\n",
    "parser.add_argument('--chunk_size', default=2, type=int,\n",
    "                        help='chunk size. for image, chunk x chunk will be the actual chunk size')\n",
    "parser.add_argument('--num_avg', default=4, type=int,\n",
    "                        help='the number of samples when perform multi-shot prediction')\n",
    "parser.add_argument('--batch_size', default=8, type=int,\n",
    "                        help='batch size')\n",
    "parser.add_argument('--env_name', default='main', type=str,\n",
    "                        help='visdom env name')\n",
    "parser.add_argument('--dataset', default='mnist', type=str,\n",
    "                        help='dataset name: imdb, mnist')\n",
    "parser.add_argument('--model_name', default='original_BUSI6.ckpt', type=str,\n",
    "                        help='model names to be interpreted')\n",
    "parser.add_argument('--explainer_type', default='cnn4', type=str,\n",
    "                        help='explainer types: nn, cnn for mnist')\n",
    "parser.add_argument('--approximater_type', default='None', type=str,\n",
    "                        help='explainer types: nn, cnn')\n",
    "parser.add_argument('--load_checkpoint', default='', type=str,\n",
    "                        help='checkpoint name')\n",
    "parser.add_argument('--checkpoint_name', default='best_acc_k10_le-5_tau0.6_beta0.001.tar', type=str,\n",
    "                        help='checkpoint name')\n",
    "parser.add_argument('--default_dir', default='.', type=str,\n",
    "                        help='default directory path')\n",
    "parser.add_argument('--data_dir', default='dataset/Dataset_BUSI_AN/train/images', type=str,\n",
    "                        help='data directory path')\n",
    "parser.add_argument('--summary_dir', default='summary', type=str,\n",
    "                        help='summary directory path')\n",
    "parser.add_argument('--checkpoint_dir', default='checkpoints', type=str,\n",
    "                        help='checkpoint directory path')\n",
    "parser.add_argument('--cuda', default=True, type=str2bool,\n",
    "                        help='enable cuda')\n",
    "parser.add_argument('--mode', default='test', type=str,\n",
    "                        help='train or test')\n",
    "parser.add_argument('--tensorboard', default=True, type=str2bool,\n",
    "                        help='enable tensorboard')\n",
    "parser.add_argument('--save_image', default=True, type=str2bool,\n",
    "                        help='if True, then save images')\n",
    "parser.add_argument('--save_checkpoint', default = True, type= str2bool,\n",
    "                        help='if True, then save checkpoint')\n",
    "parser.add_argument('--tau', default=0.7, type=float,\n",
    "                        help='tau')\n",
    "args = parser.parse_args([])\n",
    "\n",
    "torch.backends.cudnn.enabled = True\n",
    "torch.backends.cudnn.benchmark = True\n",
    "\n",
    "# print-option\n",
    "np.set_printoptions(precision=4)  # up to 4th digits for floating point output\n",
    "torch.set_printoptions(precision=4)\n",
    "print('\\n[ARGUMENTS]\\n', args)\n",
    "\n",
    "# cuda\n",
    "if torch.cuda.is_available() and not args.cuda:\n",
    "    print(\"WARNING: You have a CUDA device, so you should probably run with --cuda True\")\n",
    "args.cuda = (args.cuda and torch.cuda.is_available())\n",
    "\n",
    "net = Solver(args)\n",
    "\n",
    "if args.mode == 'train':\n",
    "    net.train(test=False)\n",
    "elif args.mode == 'test':\n",
    "    net.train(test=True)\n",
    "    # net.val(test = True)\n",
    "else:\n",
    "    print('\\n Error: \"--mode train\" or \"--mode test\" expected')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7065892a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 41\n",
      "global iter 5125\n",
      "IZY:2.71 IZX:0.00\n",
      "acc:0.7656 avg_acc:0.7656\n",
      "acc_fixed:0.5781 avg_acc_fixed:0.5781\n",
      "vmi:0.0600 avg_vmi:0.0505\n",
      "vmi_fixed:-0.2149 avg_vmi_fixed:-0.2149\n",
      "\n",
      "epoch:1\n",
      "Time spent is 24.678877592086792\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 42\n",
      "global iter 5250\n",
      "IZY:2.75 IZX:0.00\n",
      "acc:0.8281 avg_acc:0.7656\n",
      "acc_fixed:0.5938 avg_acc_fixed:0.5938\n",
      "vmi:0.0751 avg_vmi:0.0523\n",
      "vmi_fixed:-0.2271 avg_vmi_fixed:-0.2271\n",
      "\n",
      "epoch:2\n",
      "Time spent is 48.13144373893738\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 43\n",
      "global iter 5375\n",
      "IZY:2.67 IZX:0.00\n",
      "acc:0.7188 avg_acc:0.7500\n",
      "acc_fixed:0.5469 avg_acc_fixed:0.5469\n",
      "vmi:0.0285 avg_vmi:0.0518\n",
      "vmi_fixed:-0.2217 avg_vmi_fixed:-0.2217\n",
      "\n",
      "epoch:3\n",
      "Time spent is 73.05545258522034\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 44\n",
      "global iter 5500\n",
      "IZY:2.67 IZX:0.00\n",
      "acc:0.7031 avg_acc:0.7969\n",
      "acc_fixed:0.5625 avg_acc_fixed:0.5625\n",
      "vmi:0.0269 avg_vmi:0.0610\n",
      "vmi_fixed:-0.2166 avg_vmi_fixed:-0.2166\n",
      "\n",
      "epoch:4\n",
      "Time spent is 97.28594875335693\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 45\n",
      "global iter 5625\n",
      "IZY:2.74 IZX:0.00\n",
      "acc:0.8438 avg_acc:0.8281\n",
      "acc_fixed:0.5625 avg_acc_fixed:0.5625\n",
      "vmi:0.0826 avg_vmi:0.0542\n",
      "vmi_fixed:-0.2087 avg_vmi_fixed:-0.2087\n",
      "\n",
      "epoch:5\n",
      "Time spent is 121.16596055030823\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 46\n",
      "global iter 5750\n",
      "IZY:2.71 IZX:0.00\n",
      "acc:0.7656 avg_acc:0.7656\n",
      "acc_fixed:0.5781 avg_acc_fixed:0.5781\n",
      "vmi:0.0495 avg_vmi:0.0454\n",
      "vmi_fixed:-0.2021 avg_vmi_fixed:-0.2021\n",
      "\n",
      "epoch:6\n",
      "Time spent is 146.1274995803833\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 47\n",
      "global iter 5875\n",
      "IZY:2.70 IZX:0.00\n",
      "acc:0.7500 avg_acc:0.7344\n",
      "acc_fixed:0.6094 avg_acc_fixed:0.6094\n",
      "vmi:0.0522 avg_vmi:0.0596\n",
      "vmi_fixed:-0.2023 avg_vmi_fixed:-0.2023\n",
      "\n",
      "epoch:7\n",
      "Time spent is 171.67217659950256\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 48 Time since 23m 50s\n",
      "global iter 6000\n",
      "i:125 IZY:2.78 IZX:0.00\n",
      "acc:0.8750 avg_acc:0.8750\n",
      "acc_fixed:0.7500 avg_acc_fixed:0.7500\n",
      "vmi:0.1945 avg_vmi:0.1499\n",
      "vmi_fixed:0.1274 avg_vmi_fixed:0.1415\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 48\n",
      "global iter 6000\n",
      "IZY:2.62 IZX:0.00\n",
      "acc:0.6875 avg_acc:0.7656\n",
      "acc_fixed:0.5938 avg_acc_fixed:0.5938\n",
      "vmi:-0.0174 avg_vmi:0.0490\n",
      "vmi_fixed:-0.2038 avg_vmi_fixed:-0.2038\n",
      "\n",
      "epoch:8\n",
      "Time spent is 197.7862846851349\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 49\n",
      "global iter 6125\n",
      "IZY:2.75 IZX:0.00\n",
      "acc:0.7812 avg_acc:0.7656\n",
      "acc_fixed:0.5938 avg_acc_fixed:0.5938\n",
      "vmi:0.0880 avg_vmi:0.0686\n",
      "vmi_fixed:-0.1985 avg_vmi_fixed:-0.1985\n",
      "\n",
      "epoch:9\n",
      "Time spent is 223.0867223739624\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 50\n",
      "global iter 6250\n",
      "IZY:2.72 IZX:0.00\n",
      "acc:0.7812 avg_acc:0.7656\n",
      "acc_fixed:0.5938 avg_acc_fixed:0.5938\n",
      "vmi:0.0677 avg_vmi:0.0606\n",
      "vmi_fixed:-0.1994 avg_vmi_fixed:-0.1994\n",
      "\n",
      "epoch:10\n",
      "Time spent is 248.01949834823608\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 51\n",
      "global iter 6375\n",
      "IZY:2.71 IZX:0.00\n",
      "acc:0.7656 avg_acc:0.7969\n",
      "acc_fixed:0.6094 avg_acc_fixed:0.6094\n",
      "vmi:0.0568 avg_vmi:0.0747\n",
      "vmi_fixed:-0.1972 avg_vmi_fixed:-0.1972\n",
      "\n",
      "epoch:11\n",
      "Time spent is 273.3206105232239\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 52\n",
      "global iter 6500\n",
      "IZY:2.69 IZX:0.00\n",
      "acc:0.7188 avg_acc:0.8125\n",
      "acc_fixed:0.5625 avg_acc_fixed:0.5625\n",
      "vmi:0.0352 avg_vmi:0.0586\n",
      "vmi_fixed:-0.1947 avg_vmi_fixed:-0.1947\n",
      "\n",
      "epoch:12\n",
      "Time spent is 298.4323773384094\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 53\n",
      "global iter 6625\n",
      "IZY:2.76 IZX:0.00\n",
      "acc:0.8125 avg_acc:0.7812\n",
      "acc_fixed:0.5781 avg_acc_fixed:0.5781\n",
      "vmi:0.0819 avg_vmi:0.0802\n",
      "vmi_fixed:-0.1927 avg_vmi_fixed:-0.1927\n",
      "\n",
      "epoch:13\n",
      "Time spent is 323.25687527656555\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 54\n",
      "global iter 6750\n",
      "IZY:2.77 IZX:0.00\n",
      "acc:0.7969 avg_acc:0.7969\n",
      "acc_fixed:0.5781 avg_acc_fixed:0.5781\n",
      "vmi:0.1049 avg_vmi:0.0771\n",
      "vmi_fixed:-0.1915 avg_vmi_fixed:-0.1915\n",
      "\n",
      "epoch:14\n",
      "Time spent is 349.04667472839355\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 55\n",
      "global iter 6875\n",
      "IZY:2.68 IZX:0.00\n",
      "acc:0.7188 avg_acc:0.7188\n",
      "acc_fixed:0.5938 avg_acc_fixed:0.5938\n",
      "vmi:0.0250 avg_vmi:0.0602\n",
      "vmi_fixed:-0.1814 avg_vmi_fixed:-0.1814\n",
      "\n",
      "epoch:15\n",
      "Time spent is 374.7269217967987\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 56 Time since 27m 14s\n",
      "global iter 7000\n",
      "i:125 IZY:2.92 IZX:0.00\n",
      "acc:0.8750 avg_acc:0.7500\n",
      "acc_fixed:0.5000 avg_acc_fixed:0.5000\n",
      "vmi:0.3167 avg_vmi:0.2254\n",
      "vmi_fixed:-0.0172 avg_vmi_fixed:-0.0068\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 56\n",
      "global iter 7000\n",
      "IZY:2.75 IZX:0.00\n",
      "acc:0.7656 avg_acc:0.8125\n",
      "acc_fixed:0.6094 avg_acc_fixed:0.6094\n",
      "vmi:0.0810 avg_vmi:0.0869\n",
      "vmi_fixed:-0.1870 avg_vmi_fixed:-0.1870\n",
      "\n",
      "epoch:16\n",
      "Time spent is 401.0941789150238\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 57\n",
      "global iter 7125\n",
      "IZY:2.76 IZX:0.00\n",
      "acc:0.8281 avg_acc:0.7656\n",
      "acc_fixed:0.5938 avg_acc_fixed:0.5938\n",
      "vmi:0.0983 avg_vmi:0.0542\n",
      "vmi_fixed:-0.1826 avg_vmi_fixed:-0.1826\n",
      "\n",
      "epoch:17\n",
      "Time spent is 426.31082701683044\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 58\n",
      "global iter 7250\n",
      "IZY:2.71 IZX:0.00\n",
      "acc:0.7500 avg_acc:0.7031\n",
      "acc_fixed:0.5938 avg_acc_fixed:0.5938\n",
      "vmi:0.0412 avg_vmi:0.0237\n",
      "vmi_fixed:-0.1853 avg_vmi_fixed:-0.1853\n",
      "\n",
      "epoch:18\n",
      "Time spent is 451.5716998577118\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 59\n",
      "global iter 7375\n",
      "IZY:2.69 IZX:0.00\n",
      "acc:0.7656 avg_acc:0.7656\n",
      "acc_fixed:0.5781 avg_acc_fixed:0.5781\n",
      "vmi:0.0333 avg_vmi:0.0492\n",
      "vmi_fixed:-0.1831 avg_vmi_fixed:-0.1831\n",
      "\n",
      "epoch:19\n",
      "Time spent is 476.38173151016235\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 60\n",
      "global iter 7500\n",
      "IZY:2.78 IZX:0.00\n",
      "acc:0.8125 avg_acc:0.8281\n",
      "acc_fixed:0.5781 avg_acc_fixed:0.5781\n",
      "vmi:0.1067 avg_vmi:0.0902\n",
      "vmi_fixed:-0.1811 avg_vmi_fixed:-0.1811\n",
      "\n",
      "epoch:20\n",
      "Time spent is 501.53521180152893\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 61\n",
      "global iter 7625\n",
      "IZY:2.72 IZX:0.00\n",
      "acc:0.7656 avg_acc:0.8125\n",
      "acc_fixed:0.6094 avg_acc_fixed:0.6094\n",
      "vmi:0.0634 avg_vmi:0.0694\n",
      "vmi_fixed:-0.1822 avg_vmi_fixed:-0.1822\n",
      "\n",
      "epoch:21\n",
      "Time spent is 527.230242729187\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 62\n",
      "global iter 7750\n",
      "IZY:2.73 IZX:0.00\n",
      "acc:0.7812 avg_acc:0.8125\n",
      "acc_fixed:0.5938 avg_acc_fixed:0.5938\n",
      "vmi:0.0712 avg_vmi:0.0860\n",
      "vmi_fixed:-0.1808 avg_vmi_fixed:-0.1808\n",
      "\n",
      "epoch:22\n",
      "Time spent is 552.4192187786102\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 63\n",
      "global iter 7875\n",
      "IZY:2.74 IZX:0.00\n",
      "acc:0.7344 avg_acc:0.8125\n",
      "acc_fixed:0.6094 avg_acc_fixed:0.6094\n",
      "vmi:0.0835 avg_vmi:0.0931\n",
      "vmi_fixed:-0.1750 avg_vmi_fixed:-0.1750\n",
      "\n",
      "epoch:23\n",
      "Time spent is 577.1629776954651\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 64 Time since 30m 36s\n",
      "global iter 8000\n",
      "i:125 IZY:2.70 IZX:0.00\n",
      "acc:0.7500 avg_acc:0.7500\n",
      "acc_fixed:0.8750 avg_acc_fixed:0.6250\n",
      "vmi:0.1269 avg_vmi:0.1439\n",
      "vmi_fixed:0.1288 avg_vmi_fixed:0.0844\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 64\n",
      "global iter 8000\n",
      "IZY:2.79 IZX:0.00\n",
      "acc:0.7656 avg_acc:0.7500\n",
      "acc_fixed:0.5938 avg_acc_fixed:0.5938\n",
      "vmi:0.1199 avg_vmi:0.0674\n",
      "vmi_fixed:-0.1785 avg_vmi_fixed:-0.1785\n",
      "\n",
      "epoch:24\n",
      "Time spent is 602.9259870052338\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 65\n",
      "global iter 8125\n",
      "IZY:2.79 IZX:0.00\n",
      "acc:0.7969 avg_acc:0.7812\n",
      "acc_fixed:0.6094 avg_acc_fixed:0.6094\n",
      "vmi:0.1198 avg_vmi:0.0852\n",
      "vmi_fixed:-0.1745 avg_vmi_fixed:-0.1745\n",
      "\n",
      "epoch:25\n",
      "Time spent is 629.6303362846375\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 66\n",
      "global iter 8250\n",
      "IZY:2.68 IZX:0.00\n",
      "acc:0.7500 avg_acc:0.7969\n",
      "acc_fixed:0.5781 avg_acc_fixed:0.5781\n",
      "vmi:0.0228 avg_vmi:0.0562\n",
      "vmi_fixed:-0.1857 avg_vmi_fixed:-0.1857\n",
      "\n",
      "epoch:26\n",
      "Time spent is 656.469361782074\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 67\n",
      "global iter 8375\n",
      "IZY:2.74 IZX:0.00\n",
      "acc:0.7344 avg_acc:0.7344\n",
      "acc_fixed:0.5938 avg_acc_fixed:0.5938\n",
      "vmi:0.0773 avg_vmi:0.0678\n",
      "vmi_fixed:-0.1794 avg_vmi_fixed:-0.1794\n",
      "\n",
      "epoch:27\n",
      "Time spent is 682.6571543216705\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 68\n",
      "global iter 8500\n",
      "IZY:2.79 IZX:0.00\n",
      "acc:0.7812 avg_acc:0.7969\n",
      "acc_fixed:0.5938 avg_acc_fixed:0.5938\n",
      "vmi:0.1177 avg_vmi:0.0537\n",
      "vmi_fixed:-0.1817 avg_vmi_fixed:-0.1817\n",
      "\n",
      "epoch:28\n",
      "Time spent is 707.6246314048767\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 69\n",
      "global iter 8625\n",
      "IZY:2.73 IZX:0.00\n",
      "acc:0.7812 avg_acc:0.7969\n",
      "acc_fixed:0.6094 avg_acc_fixed:0.6094\n",
      "vmi:0.0734 avg_vmi:0.0651\n",
      "vmi_fixed:-0.1828 avg_vmi_fixed:-0.1828\n",
      "\n",
      "epoch:29\n",
      "Time spent is 732.4332704544067\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 70\n",
      "global iter 8750\n",
      "IZY:2.82 IZX:0.00\n",
      "acc:0.8281 avg_acc:0.7500\n",
      "acc_fixed:0.6250 avg_acc_fixed:0.6250\n",
      "vmi:0.1334 avg_vmi:0.0647\n",
      "vmi_fixed:-0.1825 avg_vmi_fixed:-0.1825\n",
      "\n",
      "epoch:30\n",
      "Time spent is 757.4781150817871\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 71\n",
      "global iter 8875\n",
      "IZY:2.86 IZX:0.00\n",
      "acc:0.8281 avg_acc:0.7500\n",
      "acc_fixed:0.6250 avg_acc_fixed:0.6250\n",
      "vmi:0.1763 avg_vmi:0.0322\n",
      "vmi_fixed:-0.1871 avg_vmi_fixed:-0.1871\n",
      "\n",
      "epoch:31\n",
      "Time spent is 782.4785332679749\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 72 Time since 34m 0s\n",
      "global iter 9000\n",
      "i:125 IZY:3.11 IZX:0.00\n",
      "acc:1.0000 avg_acc:1.0000\n",
      "acc_fixed:0.2500 avg_acc_fixed:0.5000\n",
      "vmi:-0.1793 avg_vmi:-0.1363\n",
      "vmi_fixed:-1.3599 avg_vmi_fixed:-0.8704\n",
      "test True\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 72\n",
      "global iter 9000\n",
      "IZY:2.74 IZX:0.00\n",
      "acc:0.7656 avg_acc:0.7969\n",
      "acc_fixed:0.6250 avg_acc_fixed:0.6250\n",
      "vmi:0.0778 avg_vmi:0.0560\n",
      "vmi_fixed:-0.1854 avg_vmi_fixed:-0.1854\n",
      "\n",
      "epoch:32\n",
      "Time spent is 807.4938464164734\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 73\n",
      "global iter 9125\n",
      "IZY:2.68 IZX:0.00\n",
      "acc:0.7500 avg_acc:0.7812\n",
      "acc_fixed:0.6250 avg_acc_fixed:0.6250\n",
      "vmi:0.0298 avg_vmi:0.0747\n",
      "vmi_fixed:-0.1899 avg_vmi_fixed:-0.1899\n",
      "\n",
      "epoch:33\n",
      "Time spent is 833.1035802364349\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 74\n",
      "global iter 9250\n",
      "IZY:2.68 IZX:0.00\n",
      "acc:0.7500 avg_acc:0.8281\n",
      "acc_fixed:0.6250 avg_acc_fixed:0.6250\n",
      "vmi:0.0317 avg_vmi:0.0985\n",
      "vmi_fixed:-0.1979 avg_vmi_fixed:-0.1979\n",
      "\n",
      "epoch:34\n",
      "Time spent is 858.3779797554016\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 75\n",
      "global iter 9375\n",
      "IZY:2.74 IZX:0.00\n",
      "acc:0.7812 avg_acc:0.7969\n",
      "acc_fixed:0.6562 avg_acc_fixed:0.6562\n",
      "vmi:0.0882 avg_vmi:0.0655\n",
      "vmi_fixed:-0.1937 avg_vmi_fixed:-0.1937\n",
      "\n",
      "epoch:35\n",
      "Time spent is 883.3417990207672\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 76\n",
      "global iter 9500\n",
      "IZY:2.80 IZX:0.00\n",
      "acc:0.7969 avg_acc:0.7969\n",
      "acc_fixed:0.6562 avg_acc_fixed:0.6562\n",
      "vmi:0.1225 avg_vmi:0.0836\n",
      "vmi_fixed:-0.1984 avg_vmi_fixed:-0.1984\n",
      "\n",
      "epoch:36\n",
      "Time spent is 908.4136550426483\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 77\n",
      "global iter 9625\n",
      "IZY:2.76 IZX:0.00\n",
      "acc:0.7812 avg_acc:0.8281\n",
      "acc_fixed:0.6406 avg_acc_fixed:0.6406\n",
      "vmi:0.0960 avg_vmi:0.0772\n",
      "vmi_fixed:-0.1938 avg_vmi_fixed:-0.1938\n",
      "\n",
      "epoch:37\n",
      "Time spent is 933.8462634086609\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 78\n",
      "global iter 9750\n",
      "IZY:2.74 IZX:0.00\n",
      "acc:0.7188 avg_acc:0.8125\n",
      "acc_fixed:0.6562 avg_acc_fixed:0.6562\n",
      "vmi:0.0873 avg_vmi:0.0873\n",
      "vmi_fixed:-0.1907 avg_vmi_fixed:-0.1907\n",
      "\n",
      "epoch:38\n",
      "Time spent is 958.6877176761627\n",
      "test True\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 79\n",
      "global iter 9875\n",
      "IZY:2.79 IZX:0.00\n",
      "acc:0.7812 avg_acc:0.7812\n",
      "acc_fixed:0.6562 avg_acc_fixed:0.6562\n",
      "vmi:0.1272 avg_vmi:0.0606\n",
      "vmi_fixed:-0.1873 avg_vmi_fixed:-0.1873\n",
      "\n",
      "epoch:39\n",
      "Time spent is 983.7799732685089\n",
      "\n",
      "\n",
      "[TRAINING RESULT]\n",
      "\n",
      "epoch 80 Time since 37m 22s\n",
      "global iter 10000\n",
      "i:125 IZY:2.25 IZX:0.00\n",
      "acc:0.5000 avg_acc:0.3750\n",
      "acc_fixed:0.6250 avg_acc_fixed:0.6250\n",
      "vmi:-0.3621 avg_vmi:-0.1170\n",
      "vmi_fixed:-0.1403 avg_vmi_fixed:-0.0451\n",
      "test True\n",
      "tensor([[-0.1922, -0.1843, -0.1608,  ..., -0.2392, -0.2392, -0.2314],\n",
      "        [-0.2627, -0.2627, -0.2471,  ..., -0.2157, -0.2235, -0.2235],\n",
      "        [-0.3020, -0.3098, -0.3098,  ..., -0.1686, -0.1922, -0.2000],\n",
      "        ...,\n",
      "        [-0.9294, -0.9294, -0.9451,  ..., -0.8039, -0.8039, -0.7961],\n",
      "        [-0.8431, -0.8667, -0.8902,  ..., -0.8039, -0.7961, -0.7882],\n",
      "        [-0.8039, -0.8039, -0.8039,  ..., -0.8275, -0.8353, -0.8353]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9922, -0.9922, -0.9922,  ..., -0.9529, -0.1765, -0.9922],\n",
      "        [-0.9922, -0.9922, -0.9922,  ..., -0.9529, -0.1765, -0.9843],\n",
      "        [-0.9922, -0.9922, -0.9922,  ..., -0.9608, -0.1765, -0.9922],\n",
      "        ...,\n",
      "        [-0.5922, -0.6000, -0.5608,  ..., -0.7882, -0.8196, -0.8039],\n",
      "        [-0.6000, -0.5608, -0.5529,  ..., -0.8039, -0.7647, -0.7725],\n",
      "        [-0.5294, -0.5922, -0.5922,  ..., -0.7647, -0.7961, -0.8431]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.7961, -0.8745,  0.4824,  ...,  0.6314,  0.7098,  0.7569],\n",
      "        [-0.9373, -0.9686,  0.1686,  ...,  0.6000,  0.6078,  0.6157],\n",
      "        [-0.9922, -0.9843,  0.2627,  ...,  0.3569,  0.3961,  0.3647],\n",
      "        ...,\n",
      "        [-0.7725, -0.7961, -0.8118,  ..., -0.8039, -0.8196, -0.8039],\n",
      "        [-0.7961, -0.7647, -0.7647,  ..., -0.8196, -0.8039, -0.7490],\n",
      "        [-0.8039, -0.7647, -0.7490,  ..., -0.8275, -0.8118, -0.7725]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9922, -0.9922, -0.9922,  ...,  0.5059,  0.4118, -1.0000],\n",
      "        [-0.9922, -0.9922, -0.9922,  ...,  0.3412,  0.2941,  0.2235],\n",
      "        [-0.9922, -0.9922, -0.9922,  ...,  0.3176,  0.3098,  0.2863],\n",
      "        ...,\n",
      "        [-0.7569, -0.7176, -0.7020,  ..., -0.8353, -0.8588, -0.8745],\n",
      "        [-0.7490, -0.7804, -0.7647,  ..., -0.8431, -0.8353, -0.8275],\n",
      "        [-0.7569, -0.7647, -0.7490,  ..., -0.8353, -0.8431, -0.8902]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.7020,  0.4353,  0.5765,  ...,  0.3098,  0.3412,  0.2549],\n",
      "        [ 0.4039, -0.6863, -0.9529,  ...,  0.0353,  0.0667,  0.1137],\n",
      "        [ 0.5765, -0.9608, -0.9294,  ...,  0.2078,  0.2000,  0.2000],\n",
      "        ...,\n",
      "        [-0.3725, -0.3255, -0.2627,  ...,  0.0745,  0.0431,  0.0980],\n",
      "        [-0.3098, -0.2941, -0.2392,  ..., -0.0353, -0.0118,  0.0431],\n",
      "        [-0.2627, -0.3020, -0.2784,  ..., -0.0275,  0.0510,  0.0588]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.5216, -0.8431, -0.5451,  ...,  0.7882,  0.7647,  0.8118],\n",
      "        [-0.4118, -0.8118,  0.4196,  ...,  0.7020,  0.7020,  0.7255],\n",
      "        [-0.3804, -0.7333, -0.4196,  ...,  0.6863,  0.7255,  0.7333],\n",
      "        ...,\n",
      "        [-0.8510, -0.8431, -0.8353,  ..., -0.8588, -0.8588, -0.8588],\n",
      "        [-0.8510, -0.8275, -0.8196,  ..., -0.8510, -0.8431, -0.8196],\n",
      "        [-0.8353, -0.8902, -0.8980,  ..., -0.8118, -0.8353, -0.8118]],\n",
      "       device='cuda:0')\n",
      "tensor([[ 0.2549,  0.5529,  0.6471,  ...,  0.3333, -0.7569, -0.9922],\n",
      "        [ 0.0196, -0.0431,  0.0039,  ..., -0.0118, -0.4118, -0.9922],\n",
      "        [-0.0275,  0.0118,  0.1059,  ..., -0.1294, -0.2471, -0.9922],\n",
      "        ...,\n",
      "        [ 0.0588,  0.0431,  0.0902,  ..., -0.2627, -0.3333, -0.3569],\n",
      "        [ 0.0902,  0.1686,  0.0980,  ..., -0.1765, -0.2078, -0.2078],\n",
      "        [ 0.0275,  0.1137,  0.2000,  ...,  0.1059,  0.1608,  0.1451]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9608,  0.6157, -0.6549,  ...,  0.6627,  0.6941,  0.7020],\n",
      "        [-0.9294, -0.3647,  0.7804,  ...,  0.6078,  0.6000,  0.6000],\n",
      "        [-0.9608, -0.2549, -0.9451,  ...,  0.5608,  0.4745,  0.4510],\n",
      "        ...,\n",
      "        [-0.6627, -0.7020, -0.7176,  ..., -0.8745, -0.8667, -0.8353],\n",
      "        [-0.7098, -0.7020, -0.6784,  ..., -0.9216, -0.9059, -0.8980],\n",
      "        [-0.7490, -0.7490, -0.7412,  ..., -0.8588, -0.8353, -0.8353]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.4353, -0.1137, -0.6157,  ...,  0.4118,  0.3569, -1.0000],\n",
      "        [ 0.3725, -0.4275,  0.5529,  ...,  0.2157,  0.3098,  0.0431],\n",
      "        [-0.7725, -0.2157, -0.3333,  ...,  0.2863,  0.2235,  0.2627],\n",
      "        ...,\n",
      "        [-0.3804, -0.3333, -0.2706,  ..., -0.6078, -0.6314, -0.6078],\n",
      "        [-0.3255, -0.3412, -0.3098,  ..., -0.6314, -0.6549, -0.6627],\n",
      "        [-0.3412, -0.3098, -0.2706,  ..., -0.6392, -0.6706, -0.6627]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.2078, -0.7725,  0.2941,  ...,  0.7412,  0.5765,  0.5059],\n",
      "        [-0.9922, -0.8745,  0.5765,  ...,  0.5294,  0.4039,  0.3804],\n",
      "        [-0.3804, -0.5608,  0.4275,  ...,  0.1922,  0.1373,  0.1608],\n",
      "        ...,\n",
      "        [-0.7725, -0.7882, -0.8353,  ..., -0.8353, -0.8196, -0.8039],\n",
      "        [-0.7882, -0.7725, -0.8275,  ..., -0.8118, -0.7882, -0.7961],\n",
      "        [-0.7725, -0.7882, -0.8275,  ..., -0.8196, -0.8353, -0.8353]],\n",
      "       device='cuda:0')\n",
      "tensor([[ 0.4902, -1.0000, -0.7412,  ...,  0.5216,  0.4510,  0.3882],\n",
      "        [ 0.5843, -0.9765, -0.9843,  ...,  0.3569,  0.3255,  0.3647],\n",
      "        [ 0.5059, -0.9843, -0.3961,  ...,  0.4275,  0.3569,  0.3490],\n",
      "        ...,\n",
      "        [-0.8196, -0.7882, -0.8039,  ..., -0.7804, -0.7412, -0.7647],\n",
      "        [-0.8510, -0.8510, -0.8275,  ..., -0.8039, -0.8275, -0.8196],\n",
      "        [-0.7961, -0.7725, -0.7882,  ..., -0.8275, -0.8275, -0.8353]],\n",
      "       device='cuda:0')\n",
      "tensor([[ 0.1843, -0.6706, -0.2078,  ...,  0.8902,  0.8824,  0.6157],\n",
      "        [ 0.2078, -0.6863,  0.5529,  ...,  0.8275,  0.8196,  0.7176],\n",
      "        [ 0.1686, -0.6549,  0.4745,  ...,  0.5529,  0.5451,  0.5059],\n",
      "        ...,\n",
      "        [-0.7333, -0.7412, -0.7255,  ..., -0.9373, -0.9137, -0.9059],\n",
      "        [-0.8353, -0.8118, -0.8431,  ..., -0.9686, -0.9686, -0.9686],\n",
      "        [-0.9922, -1.0000, -0.9843,  ..., -0.9922, -0.9765, -0.9686]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.2471,  0.4980, -0.9294,  ...,  0.3647,  0.4118,  0.3647],\n",
      "        [-0.7882,  0.0588, -0.5765,  ...,  0.2078,  0.2000,  0.0980],\n",
      "        [-0.9608, -0.2863, -0.3647,  ...,  0.0824, -0.0118, -0.1216],\n",
      "        ...,\n",
      "        [-0.8275, -0.8275, -0.8353,  ..., -0.8588, -0.8431, -0.8431],\n",
      "        [-0.8353, -0.8588, -0.8431,  ..., -0.8353, -0.8196, -0.7961],\n",
      "        [-0.8745, -0.8510, -0.8275,  ..., -0.8039, -0.8196, -0.7961]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9765, -0.2314, -0.1843,  ..., -0.1373, -0.2157, -0.2392],\n",
      "        [-1.0000,  0.1608,  0.3176,  ...,  0.1922,  0.1686,  0.1294],\n",
      "        [-0.7490, -0.3020, -0.2863,  ..., -0.3333, -0.3804, -0.4667],\n",
      "        ...,\n",
      "        [-0.8039, -0.7882, -0.7804,  ..., -0.7490, -0.7176, -0.7098],\n",
      "        [-0.7333, -0.7255, -0.7490,  ..., -0.6863, -0.6784, -0.6706],\n",
      "        [-0.7412, -0.7647, -0.7804,  ..., -0.7490, -0.7569, -0.7569]],\n",
      "       device='cuda:0')\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[-0.9686, -0.3098, -0.0039,  ...,  0.2471,  0.1922,  0.1843],\n",
      "        [-0.7098, -0.8824,  0.5451,  ...,  0.2471,  0.2784,  0.2471],\n",
      "        [-0.9843, -0.8510,  0.5373,  ...,  0.1451,  0.1765,  0.1529],\n",
      "        ...,\n",
      "        [-0.7412, -0.8275, -0.8745,  ..., -0.9765, -0.9529, -0.9765],\n",
      "        [-0.7490, -0.8353, -0.8667,  ..., -0.9686, -0.9843, -1.0000],\n",
      "        [-0.7961, -0.8275, -0.8275,  ..., -0.9608, -0.9608, -0.9608]],\n",
      "       device='cuda:0')\n",
      "tensor([[ 0.1922, -0.3490,  0.2549,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        [-0.8980, -0.1765,  0.2941,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        [ 0.4902, -0.8902,  0.2706,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        ...,\n",
      "        [-0.8588, -0.8667, -0.8196,  ..., -0.5843, -0.5373, -0.5608],\n",
      "        [-0.8118, -0.8196, -0.8745,  ..., -0.5608, -0.5059, -0.4745],\n",
      "        [-0.8118, -0.8039, -0.8275,  ..., -0.6157, -0.6235, -0.6078]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9216,  0.4431, -0.8118,  ...,  0.0745,  0.1373, -0.0510],\n",
      "        [-0.8824, -0.1216, -0.5137,  ...,  0.2157,  0.2235,  0.1294],\n",
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      "        ...,\n",
      "        [-0.8039, -0.8196, -0.8118,  ..., -0.8118, -0.8353, -0.8353],\n",
      "        [-0.8275, -0.8196, -0.7804,  ..., -0.8196, -0.8275, -0.8196],\n",
      "        [-0.7882, -0.8039, -0.8039,  ..., -0.8275, -0.8275, -0.8039]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.1686, -0.0353,  0.0667,  ...,  0.1922,  0.1686,  0.0510],\n",
      "        [-0.1059, -0.0667, -0.0510,  ...,  0.0275, -0.0824, -0.1922],\n",
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      "        ...,\n",
      "        [-0.4588, -0.4431, -0.3647,  ..., -0.1451, -0.0824, -0.0118],\n",
      "        [-0.5216, -0.4980, -0.4667,  ..., -0.0980, -0.0039, -0.0039],\n",
      "        [-0.6078, -0.5765, -0.5294,  ..., -0.0902, -0.0902, -0.1294]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.3961, -0.4118, -0.4118,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        [-0.2863, -0.3490, -0.3804,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        [-0.2549, -0.2471, -0.2157,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        ...,\n",
      "        [-0.7725, -0.7804, -0.8118,  ..., -0.6941, -0.6784, -0.6392],\n",
      "        [-0.7804, -0.7569, -0.7804,  ..., -0.6235, -0.6627, -0.6392],\n",
      "        [-0.6941, -0.7490, -0.7882,  ..., -0.6314, -0.6157, -0.6157]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9843, -0.9608,  0.4667,  ...,  0.2706,  0.1529, -0.9922],\n",
      "        [-0.9843, -0.9843,  0.3333,  ...,  0.1373,  0.0118, -0.9765],\n",
      "        [-0.9843, -0.4118,  0.4980,  ...,  0.2235,  0.0667, -0.2392],\n",
      "        ...,\n",
      "        [-0.6941, -0.6706, -0.6549,  ..., -0.6392, -0.6314, -0.6471],\n",
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      "        [-0.7569, -0.7490, -0.7333,  ..., -0.6706, -0.6863, -0.7020]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9529,  0.5294,  0.4431,  ...,  0.7412,  0.6784,  0.5843],\n",
      "        [-0.9451,  0.4431,  0.5373,  ...,  0.6784,  0.6627,  0.6000],\n",
      "        [-0.9373,  0.4588, -0.3725,  ...,  0.5529,  0.4902,  0.4196],\n",
      "        ...,\n",
      "        [-0.6157, -0.6314, -0.6235,  ..., -0.6784, -0.6549, -0.6157],\n",
      "        [-0.6157, -0.6706, -0.7176,  ..., -0.6627, -0.6549, -0.6157],\n",
      "        [-0.6549, -0.6627, -0.7020,  ..., -0.7098, -0.6863, -0.7020]],\n",
      "       device='cuda:0')\n",
      "tensor([[ 0.2314, -0.9922,  0.2627,  ...,  1.0000,  0.9608,  0.9216],\n",
      "        [-0.7961, -0.2471,  0.5843,  ...,  0.8275,  0.8745,  0.8353],\n",
      "        [-0.9216, -0.9608,  0.5608,  ...,  0.7020,  0.6706,  0.7098],\n",
      "        ...,\n",
      "        [-0.6392, -0.6784, -0.6863,  ..., -0.7804, -0.7569, -0.7569],\n",
      "        [-0.7333, -0.7176, -0.6627,  ..., -0.7020, -0.7176, -0.7333],\n",
      "        [-0.7490, -0.7333, -0.7098,  ..., -0.7569, -0.7020, -0.6941]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9922, -0.9765,  0.2784,  ...,  0.3569,  0.3333,  0.3725],\n",
      "        [-0.9843, -0.4431,  0.4510,  ...,  0.3961,  0.3647,  0.3255],\n",
      "        [ 0.1922,  0.5451,  0.4745,  ...,  0.3255,  0.3098,  0.2392],\n",
      "        ...,\n",
      "        [-0.8353, -0.8588, -0.8667,  ..., -0.8275, -0.7882, -0.8118],\n",
      "        [-0.8353, -0.7961, -0.7961,  ..., -0.7961, -0.8275, -0.8431],\n",
      "        [-0.7961, -0.8196, -0.8275,  ..., -0.7961, -0.8353, -0.8431]],\n",
      "       device='cuda:0')\n",
      "tensor([[-0.9922, -0.9922, -0.0510,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        [ 0.4902, -0.3882, -0.0980,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        [-0.6235,  0.4667, -0.9765,  ..., -0.9922, -0.9922, -0.9922],\n",
      "        ...,\n",
      "        [-0.8039, -0.8039, -0.8431,  ..., -0.7333, -0.7098, -0.6706],\n",
      "        [-0.8196, -0.7882, -0.7725,  ..., -0.6235, -0.6000, -0.6392],\n",
      "        [-0.8275, -0.8118, -0.7961,  ..., -0.6549, -0.7020, -0.7569]],\n",
      "       device='cuda:0')\n",
      "\n",
      "\n",
      "[VAL RESULT]\n",
      "\n",
      "epoch 80\n",
      "global iter 10000\n",
      "IZY:2.81 IZX:0.00\n",
      "acc:0.7969 avg_acc:0.7969\n",
      "acc_fixed:0.6406 avg_acc_fixed:0.6406\n",
      "vmi:0.1320 avg_vmi:0.0705\n",
      "vmi_fixed:-0.1871 avg_vmi_fixed:-0.1871\n",
      "\n",
      "epoch:40\n",
      "Time spent is 1011.7615392208099\n",
      " [*] Training Finished!\n"
     ]
    }
   ],
   "source": [
    "# 再训练100个epoch\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "net.train(test=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15533cf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "net.train(test=False)"
   ]
  }
 ],
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